social network - decideo•walk > a sequence of actors and relations that begins and ends with...
TRANSCRIPT
SOCIAL NETWORK
Michel Bruley
WA - Marketing Director
February 2012
Extract from various presentations: B Wellman, K Toyama, A Sharma, Teradata Aster, …
2 2/2/2012 Teradata Confidential
Social Network
A social network is a
social structure between
actors, mostly individuals
or organizations
It indicates the ways in
which they are connected
through various social
familiarities, ranging
from casual acquaintance
to close familiar bonds
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Society as a Graph
People are represented as
nodes
Relationships are represented
as edges: relationships may be
acquaintanceship, friendship,
co-authorship, etc.
Allows analysis using tools of
mathematical graph theory
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Little Boxes Networked Individualism Glocalization
Social Network Analysis
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities:
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Connections
• Size
> Number of nodes
• Density
> Number of ties that are present / the amount of ties that could be present
• Out-degree
> Sum of connections from an actor to others
• In-degree
> Sum of connections to an actor
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Distance
• Walk
> A sequence of actors and relations that begins and ends with actors
• Geodesic distance
> The number of relations in the shortest possible walk from one actor to another
• Maximum flow
> The amount of different actors in the neighborhood of a source that lead to pathways to a target
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Some Measures of Power & Prestige
• Degree
> Sum of connections from or to an actor
–Transitive weighted degreeAuthority, hub, pagerank
• Closeness centrality
> Distance of one actor to all others in the network
• Betweenness centrality
> Number that represents how frequently an actor is between other actors’ geodesic paths
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Cliques and Social Roles
• Cliques
> Sub-set of actors
More closely tied to each other than to actors who are not part of the sub-set:
•A lot of work on “trawling” for communities in the web-graph
•Often, you first find the clique (or a densely connected subgraph) and then try to interpret what the clique is about
• Social roles
> Defined by regularities in the patterns of relations among actors
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Network Analysis Example
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Centrality: strategic positions
Degree centrality:
Local attention
Beetweenness centrality:
reveal broker
"A place for good ideas"
Closeness centrality:
Capacity to communicate
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Social Network Analysis: what for?
• To control information flow
• To improve/stimulate communication
• To improve network resilience
• To trust
• Web applications of Social Networks examples: > Analyzing page importance (Page Rank,
Authorities/Hubs)
> Discovering Communities (Finding near-cliques)
> Analyzing Trust (Propagating Trust, Using propagated trust to fight spam - In Email or In Web page ranking)
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Tangible Outcomes from SNA
Sell More
Preserving Expertise Better Knowledge
Sharing
Building Better
Communities
More Innovation Competitive Intelligence
Organisational Re-structures
that work
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Challenges
• Large number of entities with rapidly growing amount of data for each
• Connectivity changing constantly
Aster Data Value
• SQL-MapReduce® function for Graph Analysis eases and accelerates analysis
• Ability to store and analyze massive volumes of data about users and connections
• High loading throughput and incremental loading to bring new data into analysis
Teradata Aster: See the Network
Understand connections among users and organizations
Examples
• Link analysis: predicting connections (among people, products, etc.) that are likely to be of interest by looking at known connections
• Influence analysis: identifying clusters and influencers in social networks
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Analysis of user behavior,
intent, and actions across
search, ad media and web
properties, in order to
drive increased ROI.
Teradata Aster References
Select Aster Data Customers in
Digital Marketing Optimization
Social Network & Relationship Analysis
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